Simulation Of Process Research Articles

Numerical simulation of mechanical properties and damage process of carbon nanotube concrete under triaxial compression and splitting conditions

To investigate the mechanical properties and failure processes of carbon nanotube concrete, a three-dimensional meso-scale finite element model of concrete was constructed. Using ABAQUS software, numerical simulations were conducted on concrete samples with different carbon nanotube concentrations under triaxial compression and splitting conditions. The results indicate that under splitting conditions, the failure of the specimens initially occurs along the loading direction from both ends, with few cracks and slow propagation. Subsequently, the cracks rapidly expand, penetrating the entire specimen, leading to the formation of a macro-fracture surface with a crack aligned with the loading direction. This failure mode manifests as a straight crack. At the same loading rate, the tensile strength of the specimens with different carbon nanotube concentrations increases to varying degrees during splitting failure, with a maximum increase of 45.10% to 4.15 MPa. nder triaxial compression conditions, the main failure mode of the specimens exhibits shear failure characteristics. As the confining pressure gradually increases, the failure angle of the specimens enlarges. Under low confining pressure, there are fewer wing-shaped tensile cracks at the shear failure surface of the specimens. As the confining pressure increases, irregular shear failures and multiple shear planes appear in the specimens, resulting in more complex failure modes. For specimens with the same carbon nanotube concentration, the triaxial compressive strength of carbon nanotube concrete specimens increases with increasing confining pressure. The triaxial compressive strengths of the specimens under confining pressures of 5 MPa, 10 MPa, 15 MPa, and 20 MPa are 90.48 MPa, 122.72 MPa, 137.22 MPa, and 172.65 MPa, with strength increases of 35.63%, 51.66%, and 90.81%, respectively.

Machine Learning Application and Cloud Computing-based Monitoring for Production Management in the IPC 200 Industrial Process Simulator

Industry 4.0 has revolutionized the way industrial processes are managed, introducing concepts such as advanced automation, the Internet of Things (IoT), and machine learning into production and process management. This article presents the successful incorporation of two Industry 4.0 enablers into the IPC 200 Industrial Process Simulator, a key tool in the training of students in the Electronics and Automation program at the Escuela Superior Politécnica de Chimborazo (ESPOCH). To achieve this integration, the SCRUM methodology, known for its effectiveness in managing technological projects, was employed to oversee and facilitate the implementation of the enablers, which were machine learning and cloud computing. Machine learning implementation was carried out using the Q-Learning algorithm for process optimization and data-driven decision-making, while for cloud computing, the IoT platform Ubidots was used. This allowed for greater efficiency and flexibility in managing the simulated processes in the IPC 200. The integration of these enablers opened the doors of innovation to the individuals using the simulator, allowing them to acquire new competencies in industrial process management, and preparing them to face the challenges of Industry 4.0. Furthermore, this improvement in the simulator provided a more advanced and realistic learning platform, resulting in more robust and applicable training for modern industrial environments. Keywords: Industry 4.0, Industrial Process Simulator, Node-RED, Ubidots, Q-Learning, SCRUM. Resumen La Industria 4.0 ha revolucionado la manera en que se gestionan los procesos industriales, introduciendo conceptos como la automatización avanzada, el Internet de las Cosas (IoT) y el aprendizaje automático en la producción y la gestión de procesos. En este artículo, presentamos la exitosa incorporación de dos habilitadores de la Industria 4.0 en el Simulador de Procesos Industriales IPC 200, una herramienta clave en la formación de estudiantes de la carrera de Electrónica y Automatización en la Escuela Superior Politécnica de Chimborazo (ESPOCH). Para lograr esta integración, se empleó la metodología SCRUM, conocida por su eficacia en la gestión de proyectos tecnológicos, para supervisar y facilitar la instalación de los habilitadores, los cuales fueron aprendizaje automático y computación en la nube. La implementación de aprendizaje automático se incorporó con el algoritmo de Q-Learning para la optimización de procesos y la toma de decisiones basada en datos y para la computación en la nube se utilizó la plataforma IoT Ubidots, esto permitió una mayor eficiencia y flexibilidad en la gestión de los procesos simulados en el IPC 200. La integración de estos habilitadores abrió las puertas de la innovación a las personas quienes usan el simulador, permitiendo adquirir nuevas competencias en la gestión de procesos industriales, preparándolos para enfrentar los desafíos de la Industria 4.0. Además, esta mejora en el simulador brindó una plataforma de aprendizaje más avanzada y realista, lo que resulta en una formación más sólida y aplicable a entornos industriales modernos. Palabras Clave: Industria 4.0, Simulador de Procesos Industriales, Node Red, Ubidots, Q-Learning, SCRUM.

Experimental design for the Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2)

Abstract. The Marine Ice Sheet–Ocean Model Intercomparison Project – phase 2 (MISOMIP2) is a natural progression of previous and ongoing model intercomparison exercises that have focused on the simulation of ice-sheet and ocean processes in Antarctica. The previous exercises motivate the move towards realistic configurations, as well as more diverse model parameters and resolutions. The main objective of MISOMIP2 is to investigate the performance of existing ocean and coupled ice-sheet–ocean models in a range of Antarctic environments through comparisons to observational data. We will assess the status of ice-sheet–ocean modelling as a community and identify common characteristics of models that are best able to capture observed features. As models are highly tuned based on present-day data, we will also compare their sensitivity to prescribed abrupt atmospheric perturbations leading to either very warm or slightly warmer ocean conditions compared to the present day. The approach of MISOMIP2 is to welcome contributions of models as they are, including global and regional configurations, but we request standardized variables and common grids for the outputs. We target the analysis at two specific regions, the Amundsen Sea and the Weddell Sea, since they describe two different ocean environments and have been relatively well observed compared to other areas of Antarctica. An observational “MIPkit” synthesizing existing ocean and ice-sheet observations for a common period is provided to evaluate ocean and ice-sheet models in these two regions.

Kinetic Study and Process Optimization of Plutonium Barrier Units for Enhanced Plutonium Stripping in the PUREX Process

In the PUREX (the plutonium uranium reduction extraction) process, a plutonium barrier unit (1BXX) is used to achieve deep plutonium stripping. According to the operating experience of the French reprocessing plant, after the separation of uranium and plutonium in the first cycle (1B + 1BXX), the plutonium barrier unit has excellent stripping effect, such that the removal of plutonium from uranium can already be achieved in the first cycle, and the second cycle only needs to focus on the removal of neptunium from uranium in order to obtain a qualified uranium product. In recent decades, China has also been actively conducting research on the plutonium barrier unit process to reduce the plutonium concentration in the primary uranium product in the first cycle to avoid the need to remove neptunium and plutonium at the same time in the second cycle, and to improve the efficiency and feasibility of reprocessing. Due to the lack of design basis for plutonium barriers to achieve deep plutonium stripping at present, this study conducts a basic study on the plutonium barrier unit, aiming to provide data for the optimization of plutonium barriers in the actual reprocessing process at a later date. In this work, a kinetic study on the reduction and stripping of trace plutonium from dibutyl phosphate-containing organic phases was carried out first, and the kinetic equations for the reduction and stripping of Pu(IV) by U(IV) under flow process conditions were obtained. The effects of U(IV) addition on the extraction loss of U(IV) and the concentration distribution of U(IV) at various stages were investigated by process simulation. Additionally, the oxidation of U(IV) under process conditions was investigated to clarify the process chemistry of U(IV) oxidation and to provide a reference for the oxidation consumption of U(IV). Finally, the process parameters of the plutonium barrier unit were preliminarily designed based on the above research.

Integrated framework of multi−physics mechanism models for gas−powder flow, molten pool evolution and part deformation in high alloy steel additive manufacturing process

Multi−physics numerical models for metal additive manufacturing (MAM) could replace the expensive trial−and−error experiments, reveal the mechanisms of printing process, and provide an effective means of optimizing process parameters and mitigating part defects. However, the existing simulations of MAM process seldom consider the coupling effects between multiple recursive processes. There is an urgent need to overcome the dilemma of limiting MAM process simulation to a localized single sub−process. For directed energy deposition (DED) additive manufacturing process, the integrated simulation framework proposed in this study consists of a gas−powder flow model, a heat and fluid flow model, and a sequentially coupled thermal–mechanical model, which can be used to predict the feedstock feeding process, the evolution of molten pool, and the residual deformations of metal part, respectively. The accurate results of the three recursive sub−processes can be obtained by delivering the validated parameters between models. Corresponding experiments are conducted to verify the accuracy of the integrated framework based on 12CrNi2 alloy powders. The deviation of the predicted focal distance of powder streams is 5.14 %, and the deviations of the predicted molten pool height and width are all less than 8.5 %. The integrated framework could rapidly and accurately predict the residual deformation tendency and the position where the maximum deformation occurs. For single−track and eight−layer line deposition, the deviations of the predicted maximum deformation are all within 0.1 mm. The integrated framework could efficiently provide accurate and comprehensive solutions for optimizing DED process.

Shadow Simulation of Quantum Processes.

We introduce the task of shadow process simulation, where the goal is to simulate the estimation of the expectation values of arbitrary quantum observables at the output of a target physical process. When the sender and receiver share random bits or other no-signaling resources, we show that the performance of shadow process simulation exceeds that of conventional process simulation protocols in a variety of scenarios including communication, noise simulation, and data compression. Remarkably, we find that there exist scenarios where shadow simulation provides increased statistical accuracy without any increase in the number of required samples.

Numerical simulation of a laser beam welding process: From a thermomechanical model to the experimental inspection and validation

In laser beam welding (LBW) complex physical phenomena occur during laser-material interaction, which prolong the parameterisation of the process due to the extended trial and error tests. Therefore, predicting the process behaviour leads to a more productive and cost-reduced process experimental tuning. Besides, as LBW is a thermal process, part distortion plays a relevant role in the final result, and hence, thermal and mechanical analysis are both required. In view of this, in the present research, a novel multiscale numerical model, capable of predicting the thermomechanical behaviour of the LBW process has been developed. The significance of the model is its capability of forecasting the part distortion and thermal field employing two fully coupled modules, where the laser heat source automatically adapts to the welding regime without the need to consider the melt pool dynamics and at a low computational cost. The local model determines the melt pool dimensions and thermal field. Besides, the second module, the global model, figures out the part distortion based on the thermal results. Finally, the presented numerical simulations are experimentally validated with the corresponding temperature monitoring during the welding process, the posterior metallography inspection, and the part deformation measurement. The results present a high accuracy, with a maximum error below 10 % at the temperature measurements, an average dimensional deviation of 0.14 mm and 0.18 mm respectively for the weld bead depth and width, and a vertical deformation average error of 0.15 mm.

Predicting the Consequences of an Emergency at a Railway Station

Purpose. The paper considers the problem of determining the size of the damage zones in the event of an emergency at a railway station due to a tanker fire. The task of forecasting is to determine the zones of thermal pollution, as well as chemical and mechanical pollution. The main objective of the study is to create numerical models for calculating the zones of mechanical and thermal pollution in the event of a fire at a railway station. Methodology. To analyze the size and intensity of zones of thermal, chemical, and mechanical environmental pollution in the event of an extreme situation at a railway station, we used the equations of heat and mass transfer and Newton's second law for modeling mechanical environmental pollution. To solve the equations, numerical methods such as Euler's method and finite difference schemes were used. On the basis of the developed numerical models, a computer code was created to conduct a computational experiment. Findings. Modern computer models for assessing the zones of chemical, thermal, and mechanical pollution in the event of an extreme situation have been developed. The results of computer modeling are presented. Originality. A set of numerical models for computer simulation of heat and mass transfer processes and dynamics of point motion has been created, which allows conducting a computational experiment to determine the contamination zones during a fire at a railway station. Practical value. A computer code was developed on the basis of the created mathematical models. This code is a tool for solving important problems in the field of environmental safety and civil protection. The computer code makes it possible to quickly determine the intensity and size of environmental pollution zones in the event of an extreme situation.

Humic substances and inorganic ions synergistically precipitate at the gas–liquid interface in a simulation experiment of direct-contact heat and mass transfer processes

Direct-contact heat and mass transfer processes are widely employed in saline wastewater treatment. However, these processes are accompanied by serious ultrafine particle emission, a phenomenon associated with the solute’s transition across the gas–liquid interface. A thorough comprehension of the solute’s behavior at this gas–liquid interface is vital for clarifying the crossing process and guiding particle control strategies. Here, solutions containing saturated inorganic ions and humic substances were evaporated to analyze their behaviors at the gas–liquid interface in an experimental simulation of direct-contact heat and mass transfer processes. It was found that these substances co-precipitated at this interface. Humic substances can bind with inorganic ions via chemical bonds, monovalent ions through cation-π interactions, and divalent ions through electrostatic and chelation interactions. Moreover, humic substances can act as nucleation sites for precipitating inorganic crystals. These distinctive binding mechanisms caused a synergistic relationship between humic substances and inorganic ions during precipitation. Amidst precipitation, humic substances with high humification degrees and large molecular weights were predominantly enriched. Inorganic ions, comprising over 93% of the total precipitate mass, constituted the principal constituents. Among these, Na+, with an enrichment factor of 2.10, precipitated more readily at the gas–liquid interface compared to divalent ions. These conclusions concerning the binding mechanisms of humic substances and inorganic ions, along with their precipitation characteristics, were validated in the submerged combustion evaporation process of membrane-concentrated leachate.

Web tension AI modeling and reconstruction for digital twin of roll-to-roll system

AbstractDigital twins (DT) are gaining attention as an emerging technology in Smart manufacturing systems. These DTs comprise various units that enable simulation, monitoring, and prediction of the manufacturing process. This study introduces a predictive model for web tension and a tension reconstruction algorithm for the DT of the roll-to-roll (R2R) system. The observed web tension signals from tension sensors decomposed into a mean component, a sinusoidal wave, and a random noise. Utilizing deep neural networks, the predictive model integrated various sub-models to forecast statistical (mean, standard deviation) and frequency domain (main frequency, signal-to-noise ratio) features of the web tension signal. Through fivefold cross-validation, 23 model architectures were optimized, with selected architectures ranging from 16-32-32-1 to 16-32-64-32-1 nodes per layer. Overall, R2 scores on the test set ranged from approximately 52 to 100%. The proposed reconstruction algorithm generated tension signals from the model’s predictions that closely resemble the original tension signals, indicating credible reconstructions. The proposed predictive model and reconstruction algorithm were integrated into the DT of the R2R system, offering a valuable tool for monitoring and optimizing the R2R process.

Quantitative phase-field simulation of Pu oxidation at low temperatures through a pragmatic strategy for coupling with thermodynamic and diffusion descriptions of stoichiometric compounds

In this letter, we developed a pragmatic strategy for coupling the phase-field model with the thermodynamic/kinetic descriptions and applied it to quantitatively describe the complex oxidation process of Pu. The microstructure evolution of Pu during oxidation at 423 K and 473 K was carefully simulated, and the thickness curves of various oxides were successfully predicted, which can provide the theoretical support for the storage and protection of Pu related materials. Furthermore, it is anticipated that the pragmatic strategy is universally applicable for quantitative simulation of the complex oxidation process.

Membrane Air Separation Process Simulation: Insight in Modelling Approach Based on Ideal and Mixed Permeance Values

Membrane Air Separation Process Simulation: Insight in Modelling Approach Based on Ideal and Mixed Permeance Values

Experimental study on the influencing factors of particles jetting behavior in train sanding adhesion enhancement

Sanding is the most widely used adhesion enhancing measure for trains. However, the motion of adhesion enhancement particles is vulnerable to interference from complex operating environment resulting in reduced efficiency. This work focused on the effect of sanding device parameters (sand delivery hose, air pressure and sand-blasting gun) on particles jetting behavior and particles utilization rate based on the train sanding process simulation detecting equipment. Results show that the particles utilization rate gradually improves with jetting velocity increasing and diffusion angle decreasing. Jetting velocity increases with the increase in the air pressure, nozzle diameter of sand-blasting gun and installation angle of sand delivery hose. While jetting diffusion angle decreases with the increase in the delivery hose diameter and outlet diameter of sand-blasting gun. Sanding flow rate is susceptible to the air pressure. The regression models of influencing factors on particles jetting behavior are performed for prediction and optimization.

Combustion Process of Coal–Açai Seed Mixtures in a Circulating Fluidized Bed Boiler

This study investigates the effects of the co-combustion of coal and açai seed in circulating fluidized bed (CFB) boilers, highlighting the increase in thermal efficiency and relevance of a less-polluting source of energy. Using the computer software 1.5D CeSFaMB™® v4.3.0, simulations of the co-combustion process of coal and biomass were carried out in a CFB boiler, obtaining results such as the temperature profile, boiler efficiency and emissions. The work acquired data regarding the equipment in real operational conditions, consisting of the fundamental geometric and operational parameters used in the simulation campaign. The thermal and chemical properties of the fuels were analyzed by carrying out proximate, ultimate, heating value, particle size and specific mass analyses. The model validation was achieved by simulating the boiler in its real operating conditions and comparing the obtained results with the real data; the obtained error was below 10%. Simulations with different fractions of açai seed for energy replacement (10% and 30%) were carried out. As a result, an increase in the average temperature of the bed was observed, highlighting the region immediately above the dense bed. An increase in boiler efficiency was verified from 56% to 85% with 10% açai and to 83% with 30% açai seed. Decreases in SO2 and CO emissions with the insertion of açai were obtained, showing that co-combustion is more complete, while CO2 emissions were increased due to the higher quantity of fuel inserted into the equipment. The fossil CO2 emissions were reduced.

Modeling and 3-D simulation of petroleum coke calcination process: investigation of the effect of main operating variables

Abstract Coke calcination is a process that involves heating green petroleum coke to purify it and eliminate volatile materials for subsequent processing. Due to the complexity of the rotary kiln used in this process, conducting experimental studies can be challenging and restricted. However, quantitative analyses based on developed models can provide a foundation for optimizing and controlling the process, which can significantly enhance the design efficiency. A three-dimensional simulation model of a rotary calcining kiln for petroleum coke was created using COMSOL Multiphysics in a steady-state mode. This model accounted for all relevant physical and chemical phenomena in the gas stream and coke bed flow, including heat transfer, combustion, and the evolution of volatile matter and coke dust. The mathematical modeling yielded distributions of temperature and mass fractions within the kiln, as well as the velocity field. The results revealed two distinct peak temperatures in the gas phase: 1,780 K near the primary air injection point and 1,605 K near the tertiary air injection points. The findings were analyzed, and the impact of key variables was explored. The simulation data indicated that for every decrease of 10–15 m/s in air flow, the gas peak temperature dropped by approximately 100°. Additionally, an increase in the input oxygen concentration led to enhanced combustion, resulting in higher peak concentrations of CO2. The developed simulation model has proven to be a valuable and promising tool for the design and optimization of petroleum coke rotary kilns.

Numerical simulation of hydrate flow in gas-dominated undulating pipes considering nucleation and deposition behaviors

The distribution and deposition of hydrates in deep-water gas transportation pipelines are crucial for safety. Current simulations of hydrate flow based on the population balance model have predominantly focused on water-dominant systems and neglect the nucleation and deposition of hydrates. By establishing a model for a hydrate flow in a gas-dominant system that considers the uneven distribution of the liquid film on the wall, hydrate nucleation, gas–liquid mass transfer, particle adhesion and aggregation, and dynamic deposition, we achieved simulation of the entire process of hydrate formation, aggregation, breakage, and deposition. Simulations in undulating pipelines were carried out to investigate the effects of gas velocity, liquid injection, pressure, and pipeline structure on distribution patterns. The results showed increasing the gas velocity enhanced the dispersion of particles and increasing the pressure increased the rate of aggregation. The formation of blocky aggregates posed significant risk in the rear section of the lower-bend pipe.

Exploring the impact of microfluidic chip structure on the efficacy of three-dimensional tumor microspheres cultivation.

Three-dimensional (3D) tumor microspheres can simulate the interaction and growth dynamics of tumor cells, and have been used as a new in vitro model for drug screening and tumor biology related research. The scaffold-free culture of 3D tumor microspheres on microfluidic chips has many advantages, including low cost, high throughput, convenience and flexibility. However, it is still unclear how various factors, such as chip structure, influence the culture effect of tumor microspheres. The lack of standardized evaluation and characterization of the culture effect hinders the further optimization and development of chip function. This study presents numerical simulations of multiple parts or processes of the proposed 3D culture chips with two different structural parameters based on computational fluid dynamics (CFD) methods. An evaluation system for tumor microspheres was established. The prediction of the CFD simulation was consistent with the culture results of the chips, reflecting the important role of the structural parameters of the microtrap in the formation of uniform tumor microspheres. Furthermore, the velocity of cell suspension also had a significant impact on the retention of tumor cells. Additionally, the drug screening results of tumor microspheres indicated that tumor microspheres exhibit greater drug resistance, which may be attributed to their size. These results offer valuable insights into the factors that influence the characteristics of tumor microspheres. This research provides a reference and direction for the optimal design and functional evaluation of scaffold-free 3D culture chips, and holds promise for promoting the development of novel drug research platforms.

A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

AbstractThe occurrence of process‐induced deformations of composites laminates is challenging for assembly accuracy and may lead to a service life reduction of parts. However, it can be obviously mitigated through different optimization strategies on the basis of the accurate curing process simulation. In this study, a stacking sequence optimization strategy is proposed and applied to multi‐layer thick asymmetric laminates. The shapes of deformed laminate plates are experimentally investigated in virtue of the three‐dimensional coordinate measuring machine. The thermo‐chemical–mechanical behaviors of plates are first verified through the comparisons of model predicted and experimental process‐induced deformations. Then the nonlinear control formula achieved through the regression model is proposed for the direct relationship between stacking sequences and process‐induced deformations. Finally, the required solutions are generated by solving the control formula. With the comparisons between the average deformations before and after optimizations, it is found that the magnitudes of deformations are significantly reduced, especially when the unoptimizated deformations are large.Highlights The thermo‐chemical–mechanical behavior can be described in the curing process simulation model. The deformed shapes of multi‐layer thick asymmetric laminate plates are measured by a three‐dimensional coordinate measuring machine. The stacking sequence optimization strategy is implemented by introducing a nonlinear control formula.

Process simulation and evaluation of NH3/CO2 separation in melamine tail gas using deep eutectic solvent

Melamine tail gas contains large amounts of NH3 and CO2. Its NH3 uptake is important for improvement of gas quality and resource recycling. The conventional solvent absorption and urea cogeneration methods suffer from the high energy consumption. Due to the advantages of low price, good renewability and low toxicity for deep eutectic solvents (DESs), a new absorption and separation process using NH4SCN: glycerol (2:3) DES was proposed and simulated using Aspen Plus V12™ in present contribution. Based on estimation method and experimental data, physical parameters such as density, viscosity, heat capacity, and thermal conductivity of DES were obtained. Two new process technologies, the basic DES-based process (DES-0) and the enhanced DES-based (DES-EN), were evaluated from energy and cost effectiveness. The conventional water scrubbing process (WS), DES-0, and DES-EN were systematically evaluated from process sensitivity analysis. Results demonstrated that the NH3 concentration of the products reached 99.6 % (mass fraction) for all three methods. Compared with the WS method, the cooling water usage of DES-0 was reduced by 89.16 % and the equipment cost dropped by 86.46 %. The total separation cost of the DES-0 process was 158.56 $·t−1 NH3, 79.43 % lower than that of the WS process.

A New Zero Waste Design for a Manufacturing Approach for Direct-Drive Wind Turbine Electrical Generator Structural Components

An integrated structural optimization strategy was produced in this study for direct-drive electrical generator structures of offshore wind turbines, implementing a design for an additive manufacturing approach, and using generative design techniques. Direct-drive configurations are widely implemented on offshore wind energy systems due to their high efficiency, reliability, and structural simplicity. However, the greatest challenge associated with these types of machines is the structural optimization of the electrical generator due to the demanding operating conditions. An integrated structural optimization strategy was developed to assess a 100-kW permanent magnet direct-drive generator structure. Generated topologies were evaluated by performing finite element analyses and a metal additive manufacturing process simulation. This novel approach assembles a vast amount of structural information to produce a fit-for-purpose, adaptative, optimization strategy, combining data from static structural analyses, modal analyses, and manufacturing analyses to automatically generate an efficient model through a generative iterative process. The results obtained in this study demonstrate the importance of developing an integrated structural optimization strategy at an early phase of a large-scale project. By considering the typical working condition loads and the machine’s dynamic behavior through the structure’s natural frequencies during the optimization process coupled with a design for an additive manufacturing approach, the operational range of the wind turbine was maximized, the overall costs were reduced, and production times were significantly diminished. Integrating the constraints associated with the additive manufacturing process into the design stage produced high-efficiency results with over 23% in weight reduction when compared with conventional structural optimization techniques.